#### FIGURE 2.8 WITH MODEL PREDICTIONS ####

demo_mod2 <- svyglm(Q8_mr ~ party + ideo3 + male + black + hispanic + other_race + income5 +
                      edulvl + unemployed + generation + activeduty + vet +
                      social + family + midwest + south + west + catholic + christian + norelig +
                      city + rural + married,
                    design = milconf_design)

## see below for data frames
prty_pred <- as.data.frame(predict(demo_mod2, newdata = prty, se.fit = TRUE))
prty_pred$group <- c(rep(15, 3))
prty_pred$label <- c("Democrat", "Independent", "Republican")

ideo_pred <- as.data.frame(predict(demo_mod2, newdata = ideo, se.fit = TRUE))
ideo_pred$group <- c(rep(14, 3))
ideo_pred$label <- c("Liberal", "Moderate", "Conservative")

male_pred <- as.data.frame(predict(demo_mod2, newdata = male, se.fit = TRUE))
male_pred$group <- c(rep(13, 2))
male_pred$label <- c("Female", "Male")

racw_pred <- as.data.frame(predict(demo_mod2, newdata = racw, se.fit = TRUE))
racw_pred$group <- c(12)
racw_pred$label <- c("White")
racb_pred <- as.data.frame(predict(demo_mod2, newdata = racb, se.fit = TRUE))
racb_pred$group <- c(12)
racb_pred$label <- c("Black")
rach_pred <- as.data.frame(predict(demo_mod2, newdata = rach, se.fit = TRUE))
rach_pred$group <- c(12)
rach_pred$label <- c("Hispanic")
raca_pred <- as.data.frame(predict(demo_mod2, newdata = raca, se.fit = TRUE))
raca_pred$group <- c(12)
raca_pred$label <- c("Asian")

inco_pred <- as.data.frame(predict(demo_mod2, newdata = inco, se.fit = TRUE))
inco_pred$group <- c(rep(11, 5))
inco_pred$label <- c("Q1", "Q2", "Q3", "Q4", "Q5")

educ_pred <- as.data.frame(predict(demo_mod2, newdata = educ, se.fit = TRUE))
educ_pred$group <- c(rep(10,4))
educ_pred$label <- c("HS or lower", "Some college", "College", "Graduate")

unem_pred <- as.data.frame(predict(demo_mod2, newdata = unem, se.fit = TRUE))
unem_pred$group <- c(rep(9,2))
unem_pred$label <- c("Employed", "Unemployed")

gens_pred <- as.data.frame(predict(demo_mod2, newdata = gens, se.fit = TRUE))
gens_pred$group <- c(rep(8,5))
gens_pred$label <- c("Silent", "Boomer", "Gen X", "Millennial", "Gen Z")

milc_pred <- as.data.frame(predict(demo_mod2, newdata = milc, se.fit = TRUE))
milc_pred$group <- c(rep(7,1))
milc_pred$label <- c("Civilian")
mila_pred <- as.data.frame(predict(demo_mod2, newdata = mila, se.fit = TRUE))
mila_pred$group <- c(rep(7,1))
mila_pred$label <- c("Active Duty")
milv_pred <- as.data.frame(predict(demo_mod2, newdata = milv, se.fit = TRUE))
milv_pred$group <- c(rep(7,1))
milv_pred$label <- c("Veteran")

socl_pred <- as.data.frame(predict(demo_mod2, newdata = socl, se.fit = TRUE))
socl_pred$group <- c(rep(6,2))
socl_pred$label <- c("None", "Some")

fami_pred <- as.data.frame(predict(demo_mod2, newdata = fami, se.fit = TRUE))
fami_pred$group <- c(rep(5,2))
fami_pred$label <- c("None", "Some")

rgne_pred <- as.data.frame(predict(demo_mod2, newdata = rgne, se.fit = TRUE))
rgne_pred$group <- c(rep(4,1))
rgne_pred$label <- c("Northeast")
rgmw_pred <- as.data.frame(predict(demo_mod2, newdata = rgmw, se.fit = TRUE))
rgmw_pred$group <- c(rep(4,1))
rgmw_pred$label <- c("Midwest")
rgso_pred <- as.data.frame(predict(demo_mod2, newdata = rgso, se.fit = TRUE))
rgso_pred$group <- c(rep(4,1))
rgso_pred$label <- c("South")
rgwe_pred <- as.data.frame(predict(demo_mod2, newdata = rgwe, se.fit = TRUE))
rgwe_pred$group <- c(rep(4,1))
rgwe_pred$label <- c("West")

relc_pred <- as.data.frame(predict(demo_mod2, newdata = relc, se.fit = TRUE))
relc_pred$group <- c(rep(3,1))
relc_pred$label <- c("Catholic")
relx_pred <- as.data.frame(predict(demo_mod2, newdata = relx, se.fit = TRUE))
relx_pred$group <- c(rep(3,1))
relx_pred$label <- c("Christian")
reln_pred <- as.data.frame(predict(demo_mod2, newdata = reln, se.fit = TRUE))
reln_pred$group <- c(rep(3,1))
reln_pred$label <- c("None")
relo_pred <- as.data.frame(predict(demo_mod2, newdata = relo, se.fit = TRUE))
relo_pred$group <- c(rep(3,1))
relo_pred$label <- c("Other")

geoc_pred <- as.data.frame(predict(demo_mod2, newdata = geoc, se.fit = TRUE))
geoc_pred$group <- c(rep(2,1))
geoc_pred$label <- c("Urban")
geos_pred <- as.data.frame(predict(demo_mod2, newdata = geos, se.fit = TRUE))
geos_pred$group <- c(rep(2,1))
geos_pred$label <- c("Suburban")
geor_pred <- as.data.frame(predict(demo_mod2, newdata = geor, se.fit = TRUE))
geor_pred$group <- c(rep(2,1))
geor_pred$label <- c("Rural")

mart_pred <- as.data.frame(predict(demo_mod2, newdata = mart, se.fit = TRUE))
mart_pred$group <- c(rep(1, 2))
mart_pred$label <- c("Single", "Married")

demo_pred_df <- rbind(mart_pred, geor_pred, geos_pred, geoc_pred, relo_pred, 
                      reln_pred, relx_pred, relc_pred, rgwe_pred, rgso_pred,
                      rgne_pred, rgmw_pred, fami_pred, socl_pred, milv_pred,
                      mila_pred, milc_pred, unem_pred, educ_pred, inco_pred,
                      raca_pred, rach_pred, racw_pred, racb_pred, male_pred,
                      ideo_pred, prty_pred)
demo_pred_df$ci_lo <- (demo_pred_df$link - 1.96 * demo_pred_df$SE) * 100
demo_pred_df$ci_hi <- (demo_pred_df$link + 1.96 * demo_pred_df$SE) * 100
demo_pred_df$link <- demo_pred_df$link * 100

#### DATAFRAMES ####

prty <- data.frame(party = c(0,1,2),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 3), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 3), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 3),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 3),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 3),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 3),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 3),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 3),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 3),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 3),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 3),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 3),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 3),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 3),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 3),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 3),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 3),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 3),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 3),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 3),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 3),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 3))

ideo <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 3),
                   ideo3 = c(0,1,2), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 3), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 3),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 3),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 3),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 3),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 3),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 3),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 3),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 3),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 3),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 3),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 3),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 3),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 3),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 3),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 3),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 3),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 3),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 3),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 3),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 3))

male <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 2),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 2), 
                   male = c(0,1), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 2),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 2),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 2),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 2),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 2),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 2),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 2),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 2),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 2),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 2),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 2),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 2),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 2),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 2),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 2),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 2),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 2),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 2),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 2),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 2))

racb <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 1),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 1), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 1), 
                   black = c(1),
                   hispanic = c(0),
                   other_race = c(0),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 1),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 1),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 1),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 1),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 1),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 1),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 1),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 1),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 1),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 1),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 1),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 1),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 1),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 1),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 1),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 1),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 1))

racw <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 1),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 1), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 1), 
                   black = c(0),
                   hispanic = c(0),
                   other_race = c(0),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 1),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 1),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 1),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 1),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 1),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 1),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 1),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 1),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 1),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 1),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 1),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 1),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 1),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 1),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 1),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 1),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 1))

rach <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 1),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 1), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 1), 
                   black = c(0),
                   hispanic = c(1),
                   other_race = c(0),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 1),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 1),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 1),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 1),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 1),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 1),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 1),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 1),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 1),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 1),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 1),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 1),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 1),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 1),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 1),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 1),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 1))

raca <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 1),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 1), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 1), 
                   black = c(0),
                   hispanic = c(0),
                   other_race = c(1),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 1),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 1),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 1),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 1),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 1),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 1),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 1),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 1),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 1),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 1),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 1),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 1),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 1),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 1),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 1),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 1),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 1))

inco <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 5),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 5), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 5), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 5),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 5),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 5),
                   income5 = c(1:5),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 5),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 5),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 5),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 5),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 5),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 5),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 5),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 5),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 5),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 5),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 5),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 5),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 5),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 5),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 5),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 5))

educ <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 4),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 4), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 4), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 4),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 4),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 4),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 4),
                   edulvl = c(1:4),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 4),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 4),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 4),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 4),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 4),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 4),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 4),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 4),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 4),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 4),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 4),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 4),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 4),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 4),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 4))

unem <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 2),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 2), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 2), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 2),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 2),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 2),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 2),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 2),
                   unemployed = c(0,1),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 2),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 2),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 2),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 2),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 2),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 2),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 2),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 2),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 2),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 2),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 2),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 2),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 2),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 2))

gens <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 5),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 5), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 5), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 5),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 5),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 5),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 5),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 5),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 5),
                   generation = c(1:5),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 5),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 5),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 5),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 5),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 5),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 5),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 5),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 5),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 5),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 5),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 5),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 5),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 5))

milc <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 1),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 1), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 1), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 1),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 1),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 1),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 1),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 1),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 1),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 1),
                   activeduty = c(0),
                   vet = c(0),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 1),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 1),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 1),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 1),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 1),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 1),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 1),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 1),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 1),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 1),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 1))

mila <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 1),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 1), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 1), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 1),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 1),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 1),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 1),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 1),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 1),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 1),
                   activeduty = c(1),
                   vet = c(0),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 1),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 1),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 1),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 1),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 1),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 1),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 1),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 1),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 1),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 1),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 1))

milv <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 1),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 1), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 1), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 1),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 1),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 1),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 1),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 1),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 1),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 1),
                   activeduty = c(0),
                   vet = c(1),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 1),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 1),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 1),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 1),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 1),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 1),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 1),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 1),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 1),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 1),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 1))

socl <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 2),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 2), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 2), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 2),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 2),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 2),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 2),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 2),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 2),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 2),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 2),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 2),
                   social = c(0,1),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 2),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 2),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 2),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 2),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 2),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 2),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 2),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 2),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 2),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 2))

fami <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 2),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 2), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 2), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 2),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 2),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 2),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 2),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 2),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 2),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 2),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 2),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 2),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 2),
                   family = c(0,1),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 2),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 2),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 2),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 2),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 2),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 2),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 2),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 2),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 2))

rgne <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 1),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 1), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 1), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 1),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 1),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 1),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 1),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 1),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 1),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 1),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 1),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 1),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 1),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 1),
                   midwest = c(0),
                   south = c(0),
                   west = c(0),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 1),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 1),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 1),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 1),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 1),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 1))

rgmw <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 1),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 1), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 1), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 1),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 1),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 1),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 1),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 1),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 1),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 1),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 1),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 1),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 1),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 1),
                   midwest = c(1),
                   south = c(0),
                   west = c(0),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 1),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 1),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 1),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 1),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 1),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 1))

rgso <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 1),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 1), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 1), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 1),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 1),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 1),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 1),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 1),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 1),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 1),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 1),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 1),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 1),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 1),
                   midwest = c(0),
                   south = c(1),
                   west = c(0),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 1),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 1),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 1),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 1),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 1),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 1))

rgwe <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 1),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 1), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 1), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 1),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 1),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 1),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 1),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 1),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 1),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 1),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 1),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 1),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 1),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 1),
                   midwest = c(0),
                   south = c(0),
                   west = c(1),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 1),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 1),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 1),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 1),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 1),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 1))

relc <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 1),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 1), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 1), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 1),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 1),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 1),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 1),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 1),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 1),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 1),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 1),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 1),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 1),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 1),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 1),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 1),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 1),
                   catholic = c(1),
                   christian = c(0),
                   norelig = c(0),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 1),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 1),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 1))

relx <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 1),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 1), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 1), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 1),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 1),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 1),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 1),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 1),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 1),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 1),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 1),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 1),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 1),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 1),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 1),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 1),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 1),
                   catholic = c(0),
                   christian = c(1),
                   norelig = c(0),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 1),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 1),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 1))

reln <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 1),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 1), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 1), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 1),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 1),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 1),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 1),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 1),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 1),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 1),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 1),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 1),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 1),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 1),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 1),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 1),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 1),
                   catholic = c(0),
                   christian = c(0),
                   norelig = c(1),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 1),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 1),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 1))

relo <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 1),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 1), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 1), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 1),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 1),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 1),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 1),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 1),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 1),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 1),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 1),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 1),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 1),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 1),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 1),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 1),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 1),
                   catholic = c(0),
                   christian = c(0),
                   norelig = c(0),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 1),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 1),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 1))

geoc <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 1),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 1), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 1), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 1),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 1),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 1),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 1),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 1),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 1),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 1),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 1),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 1),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 1),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 1),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 1),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 1),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 1),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 1),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 1),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 1),
                   city = c(1),
                   rural = c(0),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 1))

geor <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 1),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 1), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 1), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 1),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 1),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 1),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 1),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 1),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 1),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 1),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 1),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 1),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 1),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 1),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 1),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 1),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 1),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 1),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 1),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 1),
                   city = c(0),
                   rural = c(1),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 1))

geos <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 1),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 1), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 1), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 1),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 1),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 1),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 1),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 1),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 1),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 1),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 1),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 1),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 1),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 1),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 1),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 1),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 1),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 1),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 1),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 1),
                   city = c(0),
                   rural = c(0),
                   married = rep(mean(mil_conf.df$married, na.rm = TRUE), 1))

mart <- data.frame(party = rep(mean(mil_conf.df$party, na.rm = TRUE), 2),
                   ideo3 = rep(mean(mil_conf.df$ideo3, na.rm = TRUE), 2), 
                   male = rep(mean(mil_conf.df$male, na.rm = TRUE), 2), 
                   black = rep(mean(mil_conf.df$black, na.rm = TRUE), 2),
                   hispanic = rep(mean(mil_conf.df$hispanic, na.rm = TRUE), 2),
                   other_race = rep(mean(mil_conf.df$other_race, na.rm = TRUE), 2),
                   income5 = rep(mean(mil_conf.df$income5, na.rm = TRUE), 2),
                   edulvl = rep(mean(mil_conf.df$edulvl, na.rm = TRUE), 2),
                   unemployed = rep(mean(mil_conf.df$unemployed, na.rm = TRUE), 2),
                   generation = rep(mean(mil_conf.df$generation, na.rm = TRUE), 2),
                   activeduty = rep(mean(mil_conf.df$activeduty, na.rm = TRUE), 2),
                   vet = rep(mean(mil_conf.df$vet, na.rm = TRUE), 2),
                   social = rep(mean(mil_conf.df$social, na.rm = TRUE), 2),
                   family = rep(mean(mil_conf.df$family, na.rm = TRUE), 2),
                   midwest = rep(mean(mil_conf.df$midwest, na.rm = TRUE), 2),
                   south = rep(mean(mil_conf.df$south, na.rm = TRUE), 2),
                   west = rep(mean(mil_conf.df$west, na.rm = TRUE), 2),
                   catholic = rep(mean(mil_conf.df$catholic, na.rm = TRUE), 2),
                   christian = rep(mean(mil_conf.df$christian, na.rm = TRUE), 2),
                   norelig = rep(mean(mil_conf.df$norelig, na.rm = TRUE), 2),
                   city = rep(mean(mil_conf.df$city, na.rm = TRUE), 2),
                   rural = rep(mean(mil_conf.df$rural, na.rm = TRUE), 2),
                   married = c(0,1))
